Effective coping strategies utilised by medical students for mental health disorders during undergraduate medical education-a scoping review.

BACKGROUND: Coping denotes cognitive, emotional and behavioural struggles to tackle a troubled person-environment association. Therefore, coping strategies (CSs) are vital for mental well-being. Widespread research studies have explored this domain, targeting caregivers, nurses, physicians and medical teachers, but limited research has been done to explore the common CSs utilised by medical students at the undergraduate medical education level. Therefore, we aimed to identify the frequently occurring CSs and their effects on mental health disorders (MHDs) through the evidence available in the existing literature. METHODS: For this scoping review, we searched the available literature (articles published from January 1, 1986, to March 31, 2021) on CSs at Google Scholar, PubMed and Scopus using the terms coping, medical students and undergraduate medical education. We included in our search all peer-reviewed journal articles whose central topics were the CSs employed by undergraduate medical students of any age, nationality, race and gender. RESULTS: From among the 2,134 articles that were found, 24 were ultimately included in the study. The articles were authored in 14 countries, allowing us to gather broader data to answer our research question. The first identified theme (MHDs) had four subthemes: stress (55% of the articles), depression (30%), anxiety (25%) and burnout (15%). The second theme (CSs), on the other hand, had eight subthemes: support seeking (60%), active coping (40%), acceptance (40%), avoidance/denial (40%), substance abuse (35%), faith/religion (25%), sports (25%) and miscellaneous (40%). CONCLUSIONS: Themes and subthemes were identified about the most common CSs utilised by undergraduate medical students to tackle common MHDs in the context of medical education. Among the most used CSs was support (social and emotional) seeking. Teaching medical students how to cope with challenging times is essential.

Scoping Review of frequently highlighted attributes of Medical Professionalism in an Undergraduate Medical Education Context.

BACKGROUND AND OBJECTIVES: Medical Professionalism (MP) establishes the trust between society and doctors. We aimed at finding frequently highlighted qualities of MP in the literature. METHODS: We searched PubMed and Scopus for attributes of MP, using terms, "Professionalism," "Medical Students," and "Undergraduate Medical Education". We included English language, original research articles with MP attributes from the perspective of undergraduate medical education, any nationality, race, gender, and age range, as the central topic of the article. Papers published from January 1st 1986 to 29th February 2020 were included. RESULTS: From 1349 identified articles, finally, 18 were included, authored in 10 countries, collectively contributing to answering the scoping review question. Two themes were identified: (1) Nurturing of MP, 11 (61.11%) out of 18 included articles, highlighted "respect" as the most dominant attribute as it appeared in 6 (54.55%) out of 11 reviews, "communication" 5 (45.45 %) studies and "honesty" and "integrity" 4 (36.36%). (2) Assessment of MP, 7 (38.89%) studies, and majority, 4 (57.14 %) assessed MP using American Board of Internal Medicine's elements of MP, viz, "altruism, accountability, excellence, duty, honor and integrity, respect for others." CONCLUSIONS: Themes exemplified MP's most discoursed issues. The attributes are frequently used worldwide. MP deliberates as a commitment toward the individual patient, society, and necessitates transforming from its present generic form to more explicit details.

The roles of emotional intelligence, neuroticism, and academic stress on the relationship between psychological distress and burnout in medical students.

BACKGROUND: Stress and burnout commonly threaten the mental health of medical students in Malaysia and elsewhere. This study aimed to explore the interrelations of psychological distress, emotional intelligence, personality traits, academic stress, and burnout among medical students. METHODS: A cross-sectional study was conducted with 241 medical students. Validated questionnaires were administered to measure burnout, psychological distress, emotional intelligence, personality traits, and academic stress, respectively. A structural equation modelling analysis was performed by AMOS. RESULTS: The results suggested a structural model with good fit indices, in which psychological distress and academic stress were noted to have direct and indirect effects on burnout. The burnout levels significantly increased with the rise of psychological distress and academic stress. Neuroticism was only found to have significant indirect effects on burnout, whereby burnout increased when neuroticism increased. Emotional intelligence had a significant direct effect on lowering burnout with the incremental increase of emotional intelligence, but it was significantly reduced by psychological distress and neuroticism. CONCLUSION: This study showed significant effects that psychological distress, emotional intelligence, academic stress, and neuroticism have on burnout. Academic stress and neuroticism significantly increased psychological distress, leading to an increased burnout level, while emotional intelligence had a significant direct effect on reducing burnout; however, this relationship was compromised by psychological distress and neuroticism, leading to increased burnout. Several practical recommendations for medical educators, medical students, and medical schools are discussed.

Prevalence and associated factors of stress, anxiety and depression among emergency medical officers in Malaysian hospitals.

BACKGROUND: Demanding profession has been associated with poor psychological health due to multiple factors such as overworking hours and night shifts. This study is to determine prevalence and associated factors of depression, anxiety and stress among medical officers working at emergency department in Malaysian hospitals. METHODS: A cross-sectional study was conducted on 140 emergency department medical officers working at general hospitals from seven Malaysia regions. They were randomly selected and their depression, anxiety and stress level were measured by the 21-item Depression, Anxiety, Stress Scale. RESULTS: The highest prevalence was anxiety (28.6%) followed by depression (10.7%) and stress (7.9%). Depression, anxiety and stress between seven hospitals were not significantly different (P>0.05). Male medical officers significantly experienced more anxiety symptoms than female medical officers (P=0.0022), however depression and stress symptoms between male and female medical officers were not significantly different (P>0.05). Depression, anxiety and stress were not associated with age, working experience, ethnicity, marital status, number of shifts and type of system adopted in different hospitals (P>0.05). CONCLUSION: The prevalence of anxiety was high, whereas for depression and stress were considerably low. Gender was the only factor significantly associated with anxiety. Other factors were not associated with depression, anxiety and stress. Future research should aim to gain better understanding on unique factors that affect female and male medical officers' anxiety level in emergency setting, thus guide authorities to chart strategic plans to remedy this condition.

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

Early Initiation of Amphetamine-Type Stimulants (ATS) Use Associated with Lowered Cognitive Performance among Individuals with Co-Occurring Opioid and ATS Use Disorders in Malaysia.

Amphetamine-type stimulants (ATS) use is increasingly prevalent in Malaysia, including among individuals who also use opioids. We evaluated cognitive functioning profiles among individuals with co-occurring opioid and ATS dependence and their lifetime patterns of drug use. Participants (N = 50) enrolling in a clinical trial of buprenorphine/naloxone treatment with or without atomoxetine completed the Raven's Standard Progressive Matrices, Rey-Osterrieth Complex Figure Test, Digit Span, Trail Making and Symbol Digit Substitution tasks. Multidimensional scaling and a K-means cluster analyses were conducted to classify participants into lower versus higher cognitive performance groups. Subsequently, analyses of variance procedures were conducted to evaluate between group differences on drug use history and demographics. Two clusters of individuals with distinct profiles of cognitive performance were identified. The age of ATS use initiation, controlling for the overall duration of drug use, was significantly earlier in the lower than in the higher cognitive performance cluster: 20.9 (95% CI: 18.0-23.8) versus 25.2 (95% CI: 22.4-28.0, p = 0.038). While adverse effects of ATS use on cognitive functioning can be particularly pronounced with younger age, potentially related to greater vulnerability of the developing brain to stimulant and/or neurotoxic effects of these drugs, the current study findings cannot preclude lowered cognitive performance before initiation of ATS use.

The Opposing Roles of IVS2+691 CC Genotype and AC/AG Diplotype of 118A>G and IVS2+691G>C of OPRM1 Polymorphisms in Cold Pain Tolerance Among Opioid-Dependent Malay Males on Methadone Therapy.

INTRODUCTION: We recently reported that a majority of opioid-dependent Malay males on methadone therapy are cold pain sensitive. It is postulated that common OPRM1 polymorphisms may be responsible. This study investigated the association between 118A>G (dbSNP rs1799971) and IVS2+691G>C (dbSNP rs2075572) variants on cold pain responses among opioid-dependent Malay males on methadone maintenance therapy. METHODS: Cold pain responses including pain threshold, pain tolerance, and pain intensity were measured using the cold pressor test. DNA was extracted from the venous blood before polymerase chain reaction genotyping. Repeated measures analysis of variance was used to compare the cold pain responses and OPRM1 polymorphisms (118A>G and IVS2+691G>C) using models including genotype dominant and recessive models, allelic additive models, and analysis of haplotypes and diplotypes. RESULTS: A total of 148 participants were recruited. With the recessive model, those with IVS2+691 homozygous CC genotype had a shorter cold pain tolerance time than those without CC genotype (i.e., GG/GC genotype; 29.81 vs. 43.08 s, respectively, P = 0.048). On the other hand, with diplotype analysis, participants with combined homozygous 118 AA genotype and heterozygous IVS2+691 GC genotype (i.e., AC/AG diplotype) had a longer cold pain tolerance time than those without this diplotype (49.34 vs. 31.48 s, respectively, P = 0.043). Cold pain threshold was not associated with any of the 118A>G and IVS2+691G>C variations despite being analyzed using various models (all P > 0.05). CONCLUSION: The IVS2+691 CC genotype and AC/AG diplotype of 118A>G and IVS2+691G>C seem to have opposing roles in pain tolerance among opioid-dependent Malay males on methadone therapy. Haplotypes of OPRM1 may be associated with altered binding affinity.

Detrended fluctuation analysis for major depressive disorder.

Clinical utility of Electroencephalography (EEG) based diagnostic studies is less clear for major depressive disorder (MDD). In this paper, a novel machine learning (ML) scheme was presented to discriminate the MDD patients and healthy controls. The proposed method inherently involved feature extraction, selection, classification and validation. The EEG data acquisition involved eyes closed (EC) and eyes open (EO) conditions. At feature extraction stage, the de-trended fluctuation analysis (DFA) was performed, based on the EEG data, to achieve scaling exponents. The DFA was performed to analyzes the presence or absence of long-range temporal correlations (LRTC) in the recorded EEG data. The scaling exponents were used as input features to our proposed system. At feature selection stage, 3 different techniques were used for comparison purposes. Logistic regression (LR) classifier was employed. The method was validated by a 10-fold cross-validation. As results, we have observed that the effect of 3 different reference montages on the computed features. The proposed method employed 3 different types of feature selection techniques for comparison purposes as well. The results show that the DFA analysis performed better in LE data compared with the IR and AR data. In addition, during Wilcoxon ranking, the AR performed better than LE and IR. Based on the results, it was concluded that the DFA provided useful information to discriminate the MDD patients and with further validation can be employed in clinics for diagnosis of MDD.